1. Technical Field
The present invention is directed to an adaptive howling canceller for use in preventing howling from developing in a sound-reinforcement system installed in auditoria, halls and the like.
2. Related Art
Hitherto, there are known adaptive howling cancellers for preventing howling from developing by using an adaptive filter (adaptive digital filter). Such a technology is disclosed for*example in non-patent document of Inazumi, Imai, and Konishi: “howling prevention in a sound-reinforcement system using the LMS algorithm”, Acoustical Society of Japan, proceedings pp. 417-418 (1991, 3).
FIG. 12 shows a schematic circuit diagram of a sound-reinforcement system with the type of howling canceller equipped. A microphone 1 and a speaker 4 are placed in a given room. The audio signal input through the microphone 1 is transformed to a signal y(k) in a digital domain through an A/D (analog to digital) conversion process. The y(k) represents a signal at the time kT (where T designates a sampling interval of the audio signal). The signal y(k) is supplied through an adder 2 to an amplifier 3 for amplification. G(z) represents the transfer function of the amplifier 3. The signal x(k) output from the amplifier 3 will be converted to the signal in analog domain by means of a D/A (digital to analog) conversion process, then this electric signal is transformed by the speaker 4 to the acoustic signal.
The acoustic feedback loop 5 is an acoustic path from the speaker 4 to the microphone 1, which has a transfer function H(z). The feedback acoustic signal d(k) fed back through the acoustic feedback loop 5 will be intermixed with the acoustic source signal s(k) composed of the audio signal from the audio source such as a narrator, prior to input into the microphone 1. The microphone 1 will transform the intermixed audio signal from the input to output the electric signal.
The sound-reinforcement system as have been described above may establish a closed loop composed of the path from the microphone 1 through amplifier 3 to speaker 4 then through acoustic feedback loop 5 to microphone 1, resulting in a developed howling due to the increase of the feedback acoustic signal d(k). The adaptive howling canceller has been devised in order to prevent the development of such howling, which includes a delay 6, an adaptive filter 7, and an adder 2.
The delay 6 may output the signal x(k) with a time delay τ in correspondence with the amount of time delay in the acoustic feedback loop 5, and the output signal x(k-τ) will be supplied to the adaptive filter 7. The adaptive filter 7 includes a digital filter 7a and a filter coefficient estimation unit 7b, as shown in FIG. 13, the signal x(k-τ) is input to both of the digital filter 7a and the filter coefficient estimation unit 7b. The digital filter 7a outputs a signal do(k) that simulates the feedback audio signal d(k), in accordance with the transfer function F(z), and the signal do(k) will be subtracted from the signal y(k) by the adder 2. The signal y(k) can be represented by an expression as y(k)=s(k)+d(k). The output signal e(k) of the adder 2 can be represented by an expression as e(k)=y(k)−do(k)=s(k)+Δ(k) {where Δ(k)=d(k)−do(k)}. Accordingly, the signal e(k) will be substantially equal to s(k) without the influence of the signal d(k), provided that Δ(k) is sufficiently small, to allow preventing the development of howling. Without the delay 6, the audio source signal s(k) input into the microphone 1 will be input to the adder 2 while also inputting into the adaptive filter 7 with no delay. Since the adaptive filter 7 updates the filter coefficient so as to decrease an error signal e(k), along with the progress of update of the filter coefficient, the audio source signal s(k) in the adder 2 will become canceled by the output signal from the adaptive filter 7. For this reason, the delay 6 is indispensable in order to cancel the feedback audio signal d(k) with the signal do(k) while at the same time preventing the audio source signal s(k) from being canceled.
The filter coefficient estimation unit 7b recurrently updates the filter coefficient of the digital filter 7a so that the transfer function F(z) matches with or approximate to the transfer function H(z) by using the adaptive algorithm and based on the signals x(k-τ) and e(k). The exemplary adaptive algorithm used includes for example LMS (least mean square) algorithm. When the mean square value of the signal e(k) is represented by J=E [e (k)2] (where E[*] indicates an expectation value), the filter coefficient that makes J minimum will be estimated by computation to update the filter coefficient of the digital filter 7a by using thus estimated filter coefficient. As a result of this, a signal that simulates the signal d(k) can be derived for the signal do(k), allowing the howling to be prevented from developing.
In accordance with the prior art described above, when using an adaptive filter 7 which is shorter (has smaller number of taps) as compared with the transfer function H(z), there may arise a problem that the sound quality is severely affected. The inventors of the present invention have conducted an experimental simulation of howling prevention by means of the sound-reinforcement system as shown in FIG. 12.
FIG. 11 shows the result of the experimental simulation. In FIG. 11, when e2(k) in the ordinate is read as e(k), FIG. 11 indicates the change over time of the signal e(k). In the experimentation, the transfer function H(z) has 48,000 taps set, and the adaptive filter 7 has the number of taps of 256, respectively. In FIG. 11, there is no divergence of amplitude, indicating that the howling has been prevented from developing. However, since the adaptive filter 7 simulates only 256 taps of the head part with respect to the transfer function H(z) that has total 48,000 taps, the simulation of the transfer function H(z) is not sufficient so that the signal e(k) has a higher level and the sound quality is significantly affected.
In order to decrease the influence to the sound quality, it is sufficient to approximate the number of taps of the adaptive filter 7 to the entire length of transfer function H(z). However, since LMS algorithm updates the filter coefficient for each sample, the update interval is obviously short (the time to compute a new filter coefficient is short), while the amount of computation per unit time (will be abbreviated as “amount of computation” herein below) required for the update of filter coefficient increases in proportion to the number of taps. Accordingly, in a room where the transfer function H(z) is respectively long (namely, the reverberation time is relatively long) the number of taps is limited by the amount of computation, and the number of taps cannot be increased even if one attempts to increase the number of taps so as to bring it closer to the length of transfer function H(z). Therefore, the sound quality is severely affected and the sound quality is inevitably decreased.
On the other hand, for the adaptive algorithm, there are known algorithms which have a much longer update interval to update the filter coefficient for every tens of thousands samples, such as STFT-CS (Short Time Fourier Transform and Cross Spectrum), and it can be conceivable to update the filter coefficient of the adaptive filter 7 by using one of such algorithms. In such a case, the filter coefficient can be updated with less amount of computation even when the number of taps of the adaptive filter is increased, so that the transfer function can be simulated sufficiently for a room which has a long transfer function (i.e., long reverberation time) while at the same time the sound quality can be less affected. However, if the howling develops much quicker than the update period of filter coefficient, the update of filter coefficient is likely to delay when compared to the development of howling, some howling might be developed transitorily.